The ubiquity of cortico-cortical feedback connections argues strongly for their importance, and theoreticians routinely make use of them in models of cortical function. Despite this, next to nothing is known about their function. Previous studies in which feedback has been manipulated have been performed in anesthetized animals, and our recent studies in alert animals have revealed fundamentally different results. Our major finding from the previous cycle is that feedback from V2 exerts minimal effects on the classically defined receptive field center of V1 neurons: orientation tuning and the center's contrast response function are virtually unaffected. However, larger stimuli that engage the receptive field surround and that normally cause suppression, cause much less suppression when V2 is inactivated. We have further discovered that the influence of feedback is on the spatial extent of the surround, rather than on its gain, and that seemingly higher order properties of surround suppression, such as feature- matched suppression, are more strongly influenced by feedback than is basic normalization. All of these observations are consistent with a theoretical framework of predictive coding, which posits a key computational role for the interaction between feedforward and feedback connections. We now propose to examine specific examples ranging from built-in contextual influences in form and motion perception to the nature of changes that underlie the learning of a perceptual task. Our general approach will be to use causal manipulations-reversible inactivation of cortical areas (V2 and/or MT) that feed back to V1-to test the hypothesized role of top-down influences in perceptual inference. Our studies will shed light on the basic mechanisms of cortical function.